Data Labeling
The process of adding annotations or tags to data to create training datasets for supervised learning. Labels tell the model what output to predict for each input.
In-Depth Explanation
Data labeling (annotation) adds ground truth labels to data, creating supervised learning datasets. It's often the most time-consuming and expensive part of ML projects.
Labeling types:
- Classification: Assigning categories
- Bounding boxes: Drawing rectangles around objects
- Segmentation: Pixel-level annotation
- Named entity: Tagging text spans
- Sentiment: Rating emotional tone
- Relationships: Connecting entities
Labeling approaches:
- Manual: Human annotators
- Crowdsourcing: Distributed workers
- Automated: Model-assisted suggestions
- Weak supervision: Programmatic rules
- Active learning: Smart sample selection
Labeling platforms:
- Scale AI, Labelbox, Appen
- Amazon SageMaker Ground Truth
- Open source: Label Studio, CVAT
Business Context
Labeling quality determines model quality. Budget for labeling as a significant project cost - it's often 50%+ of data preparation effort.
How Clever Ops Uses This
We design efficient labeling workflows for US businesses, balancing cost, quality, and speed for AI training data creation.
Example Use Case
"Setting up a labeling workflow where domain experts label 100 examples, then model suggestions accelerate labeling of remaining 5,000."